Effects of gene–environment and gene–gene interactions in case-control studies: A novel Bayesian semiparametric approach

Abstract

Present day bio-medical research is pointing towards the fact that virtually almost all diseases are manifestations of complex interactions of genetic susceptibility factors and modifiable environmental conditions. Cognizance of gene-environment interactions may help prevent or detain the onset of complex diseases like cardiovascular disease, cancer, type2 diabetes, autism or asthma by adjustments to lifestyle. In this regard, we extend the Bayesian semiparametric gene-gene interaction model of Bhattacharya & Bhattacharya (2016) to detect not only the roles of genes and their interactions, but also the possible influence of environmental variables on the genes in case-control studies. Our model also accounts for the unknown number of genetic sub-populations via finite mixtures composed of Dirichlet processes, which are related to each other through a hierarchical matrix-normal structure, incorporating gene-gene and gene-environment interactions. An effective parallel computing methodology, developed by us harnesses the power of parallel processing technology to increase the efficiencies of our conditionally independent Gibbs sampling and Transformation based MCMC (TMCMC) methods.Applications of our model and methods to simulation studies with biologically realistic casecontrol genotype datasets obtained under five distinct set-ups of gene-environment interactions action yield encouraging results in each case. We followed these up by application of our ideas to a real, case-control based genotype dataset on early onset of myocardial infarction. Beside being in broad agreement with the reported literature on this dataset, the results obtained give some interesting insights to the differential effect of gender on MI.

A Bayesian semiparametric approach to learning about gene–gene interactions in case-control studies

“…In this work we have focused exclusively on gene-gene interaction. Recently, Bhattacharya & Bhattacharya (2017b) and Bhattacharya & Bhattacharya (2017a) have extended this model to incorporate gene-environment interactions in our model, and developed tests for the effects of geneenvironment interactions as well as gene-gene interactions on case-control. They have successfully applied the ideas to various simulated datasets generated from the GENS2 software, and to the MI dataset, considering sex as the environmental variable.…”

“…Furthermore, we develop novel Bayesian hypotheses testing procedures and associated methodologies to investigate the effects of genes on complex diseases in the context of case-control dataset arising from a possibly stratified population of genotypes. In what follows we investigate only the genetic effects on complex diseases, without taking into account the environmental effects (but see Bhattacharya & Bhattacharya (2017b) and Bhattacharya & Bhattacharya (2017a)).…”